escalation_server.py

# from mcp.server.fastmcp import FastMCP 
from fastmcp import FastMCP, Context
from fastmcp.dependencies import CurrentContext
import os
import smtplib
from email.message import EmailMessage
import aiosmtplib
import httpx

# --- EMAIL CONFIGURATION ---
SMTP_SERVER = os.getenv("SMTP_SERVER", "smtp.gmail.com")
SMTP_PORT = int(os.getenv("SMTP_PORT", "587"))

SENDER_EMAIL = os.getenv("SENDER_EMAIL")
SENDER_PASSWORD = os.getenv("SENDER_PASSWORD")

# Fail fast if credentials are missing
if not SENDER_EMAIL or not SENDER_PASSWORD:
    raise ValueError("SMTP credentials are not set in environment variables.")

# Initialize Server
mcp = FastMCP("escalation_server")

# Path Configuration
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
RESOURCE_PATH = os.path.join(CURRENT_DIR, "resource", "jemh114-min (1).md")

# --- CENTRALIZED REGISTRY ---
FACULTY_DATA = {
    "Mr. Murty":  {"email": "dharmin.naik@webashlar.com", "id": "T-MURTY-01"},
    "Mrs. Sunita":{"email": "dummyaiapi03@gmail.com", "id": "T-SUNITA-03"},
    "Mr. Patel":  {"email": "dev.patel@webashlar.com", "id": "T-PATEL-05"},
    "Dr. Rao":    {"email": "pratham.lakdawala@webashlar.com", "id": "T-RAO-02"},
    "Mr. Khanna": {"email": "dummyuseai@gmail.com", "id": "T-KHANNA-04"},
    "Ms. Joshi":  {"email": "dummyaiapi532@gmail.com", "id": "T-JOSHI-06"}
}

CLASS_ASSIGNMENTS = {
    "10-A": {"Math": "Mr. Murty", "Science": "Dr. Rao"},
    "10-B": {"Math": "Mrs. Sunita", "Science": "Mr. Khanna"},
    "10-C": {"Math": "Mr. Patel", "Science": "Ms. Joshi"}
}

# Input Normalization Map
SUBJECT_MAP = {
    "mathematics": "Math",
    "math": "Math",
    "maths": "Math",
    "statistics": "Math", 
    "stats": "Math",      
    "science": "Science",
    "physics": "Science", # For this demo, mapping physics to general science
    "biology": "Science"
}

def normalize_subject(subject: str) -> str:
    """Standardizes subject names so 'Mathematics' becomes 'Math'."""
    return SUBJECT_MAP.get(subject.lower(), subject.title())

# --- 1. MCP RESOURCES ---
# Resources are "read-only" data sources the LLM can reference.
def read_resource_file() -> str:
    if not os.path.exists(RESOURCE_PATH):
        return "Error: Statistics markdown file not found."
    with open(RESOURCE_PATH, "r", encoding="utf-8") as f:
        return f.read()

@mcp.resource("study://math/statistics")
def statistics_resource() -> str:
    """Exposes the material to the LLM as a read-only resource."""
    return read_resource_file()

# @mcp.resource("study://math/statistics")
# def get_stats_material() -> str:
#     """
#     Exposes the Class X Statistics chapter. 
#     The AI uses this to find formulas for Mean, Median, and Mode.
#     """
#     if not os.path.exists(RESOURCE_PATH):
#         return "Error: Statistics markdown file not found at the specified path."
        
#     with open(RESOURCE_PATH, "r", encoding="utf-8") as f:
#         content = f.read()
        
#     return content

@mcp.resource("system://tutor/instructions")
def tutor_instructions() -> str:
    """The mandatory operational manual for this tutoring session."""
    return """
    # OPERATIONAL PROTOCOL
    1. You are a gateway, not a teacher. 
    2. Max 3 attempts allowed using 'explain_from_resource' or 'generate_quick_revision'.
    3. If a tool returns 'LIMIT_REACHED', immediately ask for Class ID.
    4. Use get_teacher_info -> escalate_to_teacher.
    5. NO internal brainstorming. Use the provided textbook material ONLY.
    """

# --- 2. MCP TOOLS ---
@mcp.tool()
async def explain_from_resource(concept: str, ctx: Context = CurrentContext()):
    # 1. Properly await the state
    state_val = await ctx.get_state("math_attempts")
    attempts = state_val or 0

    # 2. Comparison now works because 'attempts' is an integer
    if attempts >= 3:
        # FIX: Changed 'tools=' to 'names='
        await ctx.disable_components(names=["explain_from_resource", "generate_quick_revision"])
        return (
            "FATAL_ERROR: TUTORING_LIMIT_REACHED. "
            "Please request Class ID and notify the teacher."
        )

    # 3. Properly await the update
    new_attempts = attempts + 1
    await ctx.set_state("math_attempts", new_attempts)
    
    material = read_resource_file() 
    return f"""
    <attempt>{new_attempts}/3</attempt>
    <content>{material}</content>
    <instruction>Explain {concept} concisely. Use LaTeX.</instruction>
    """

@mcp.tool()
async def generate_quick_revision(type: str = "summary", ctx: Context = CurrentContext()) -> str:
    # 1. Properly await the state
    state_val = await ctx.get_state("math_attempts")
    attempts = state_val or 0
    
    if attempts >= 3:
        # FIX: Changed 'tools=' to 'names='
        await ctx.disable_components(names=["explain_from_resource", "generate_quick_revision"])
        return "FATAL_ERROR: TUTORING_LIMIT_REACHED. Transition to escalation immediately."

    # 2. Properly await the update
    new_attempts = attempts + 1
    await ctx.set_state("math_attempts", new_attempts)

    material = read_resource_file()
    return f"""
    <attempt>{new_attempts}/3</attempt>
    <material>{material}</material>
    <request>Create a {type} for quick revision.</request>
    """

def normalize_class_id(class_id: str) -> str:
    """Converts 'Class 10B', '10b', or 'ten b' into '10-B'."""
    clean = class_id.upper().replace("CLASS", "").replace("GRADE", "").replace(" ", "").strip()
    # If student just says "10", we can't know which section, 
    # but we can format "10B" to "10-B"
    if len(clean) == 3 and clean[2].isalpha():
        return f"{clean[:2]}-{clean[2:]}"
    return clean

# --- 3. ESCALATION TOOLS (From our previous discussion) ---
@mcp.tool()
async def get_teacher_info(subject: str, class_id: str) -> dict:
    """
    Finds the specific teacher for a subject and class.
    Ensures Math queries go to Math teachers and Science to Science teachers.
    """
    # teacher_name = CLASS_ASSIGNMENTS.get(class_id, {}).get(subject)
    std_subject = normalize_subject(subject)
    std_class = normalize_class_id(class_id)
    
    # Verify class exists first
    # class_data = CLASS_ASSIGNMENTS.get(class_id)
    class_data = CLASS_ASSIGNMENTS.get(std_class)
    # if not class_data:
    #     return {"error": f"Class {class_id} does not exist in our registry."}
    if not class_data:
        # NEW: Suggest valid classes if the lookup fails
        valid_classes = ", ".join(CLASS_ASSIGNMENTS.keys())
        return {"error": f"Class '{std_class}' not found. Please use one of: {valid_classes}"}
    
    teacher_name = class_data.get(std_subject)
    if not teacher_name:
        return {"error": f"No {std_subject} teacher assigned to Class {class_id}."}
    
    teacher_details = FACULTY_DATA.get(teacher_name)
    return {
        "name": teacher_name,
        "id": teacher_details["id"],
        "subject": subject
    }

# --- RESEND EMAIL CONFIGURATION ---
# RESEND_API_KEY = os.getenv("RESEND_API_KEY")
VERCEL_EMAIL_API_URL = os.getenv("VERCEL_EMAIL_API_URL", "https://smtp-email-service-a61d.vercel.app/api/send_email")
@mcp.tool()
async def escalate_to_teacher(
    teacher_name: str,
    subject: str,
    student_query: str,
    ai_response: str,
    ctx: Context = CurrentContext()
) -> str:
    """Sends a real-time email via the Vercel SMTP Bridge API."""
    
    # 1. Get recipient from registry
    teacher_info = FACULTY_DATA.get(teacher_name)
    recipient = teacher_info["email"] if teacher_info else "dummyuseai@gmail.com"

    # 2. Prepare the email content
    email_subject = f"🚨 Escalation: {subject} Doubt for {teacher_name}"
    html_body = f"""
    <html>
        <body style="font-family: Arial, sans-serif; color: #333;">
            <div style="background: #f9f9f9; padding: 20px; border: 1px solid #ddd;">
                <h2 style="color: #d9534f;">Hello {teacher_name},</h2>
                <p>A student has a <strong>{subject}</strong> doubt that requires your attention.</p>
                <hr>
                <p><strong>Doubt:</strong> {student_query}</p>
                <p><strong>AI Response:</strong> {ai_response}</p>
            </div>
        </body>
    </html>
    """

    # 3. Construct the payload for your Vercel API
    # Based on your index.py: to, subject, body, is_html
    api_payload = {
        "to": recipient,
        "subject": email_subject,
        "body": html_body,
        "is_html": True
    }

    # 4. Call the Vercel API via HTTPS
    async with httpx.AsyncClient() as client:
        try:
            response = await client.post(
                VERCEL_EMAIL_API_URL, 
                json=api_payload,
                timeout=30.0
            )
            
            if response.status_code == 200:
                # --- SUCCESS: RESET LOGIC ---
                await ctx.set_state("math_attempts", 0)
                
                # Re-enable tutoring tools if they were disabled
                await ctx.enable_components(names=["explain_from_resource", "generate_quick_revision"])
                
                return f"Success: {teacher_name} ({subject} Expert) has been notified via Vercel Bridge."
            else:
                # Handle API-level errors (400, 401, 502, etc.)
                error_data = response.json()
                return f"Vercel API Error ({response.status_code}): {error_data.get('error', 'Unknown error')}"

        except httpx.ConnectError:
            return "Critical Error: Could not connect to Vercel API. Check your VERCEL_EMAIL_API_URL."
        except Exception as e:
            return f"Unexpected Error: {str(e)}"

# @mcp.tool()
# async def escalate_to_teacher(
#     teacher_name: str,
#     subject: str,
#     student_query: str,
#     ai_response: str,
#     ctx: Context = CurrentContext()
# ) -> str:

#     """Sends a real-time email to the correct teacher by name."""
    
#     # Safely get email from our central registry
#     teacher_info = FACULTY_DATA.get(teacher_name)

#     if not teacher_info:
#         recipient = "dummyuseai@gmail.com" # Fallback to admin
#     else:
#         recipient = teacher_info["email"]

#     msg = EmailMessage()
#     msg["Subject"] = f"🚨 Escalation: {subject} Doubt for {teacher_name}"
#     msg["From"] = SENDER_EMAIL
#     msg["To"] = recipient

#     html_content = f"""
#     <html>
#     <body style="font-family: Arial, sans-serif; color: #333;">
#     <div style="background: #f9f9f9; padding: 20px; border: 1px solid #ddd;">
#     <h2 style="color: #d9534f;">Hello {teacher_name},</h2>
#     <p>A student has a <strong>{subject}</strong> doubt that requires your attention.</p>
#     <hr>
#     <p><strong>Doubt:</strong> {student_query}</p>
#     <p><strong>AI Response:</strong> {ai_response}</p>
#     </div>
#     </body>
#     </html>
#     """
#     msg.add_alternative(html_content, subtype="html")

#     try:
#         await aiosmtplib.send(msg,
#         hostname=SMTP_SERVER,
#         port=SMTP_PORT,
#         username=SENDER_EMAIL,
#         password=SENDER_PASSWORD,
#         start_tls=True,
#         use_tls=False,
#         timeout=30
#         ) 
#         # --- THE FIX: RESET LOGIC ---
#         # ctx.set_state("math_attempts", 0) # Reset counter to 0
#         await ctx.set_state("math_attempts", 0)
#         # await ctx.enable_components(tools=["explain_from_resource", "generate_quick_revision"]) # Re-enable tools
#         return f"Success: {teacher_name} ({subject} Expert) has been notified."
#     except Exception as e:
#         return f"Error sending email: {str(e)}"

# --- 4. MCP PROMPTS ---
@mcp.prompt()
def revision_assistance(student_name: str):
    """A pre-configured prompt to help students with exam prep."""
    return f"Hi {student_name}, I can help you summarize your chapter or create short notes for formulas. Which one would you like to do first?"

@mcp.prompt()
def escalation_handover(teacher_name: str, subject: str) -> str:
    """Professional message used when handing over to a human teacher."""
    return (
        f"I've shared our {subject} discussion with {teacher_name}. "
        "They will review it and get back to you on your dashboard soon!"
    )

# if __name__ == "__main__":
#     mcp.run()
if __name__ == "__main__":
    # For Hugging Face Spaces, we use transport="sse" or "http"
    # Port 7860 is the HF default
    mcp.run(
        transport="http", 
        port=7860, 
        host="0.0.0.0"
    )